Learning to reorder alternates based on a user's personalized vocabulary is a process for suppressing unused words in a system lexicon of a text recognition application using statistical learning methodologies and a user's personal data. When using an alternative input system, for example, a speech recognition system or a handwriting recognition system, the system lexicon is used to find an N best set of words that may match a user's input and present to the words to the user in the form of an ‘alternate’ list.
In such systems, the system lexicon is designed to include words that are used by a wide range of users. As a result, the conventional system lexicon constantly accumulates words that can be provided as alternates to the user. However, the conventional system lexicon often provides alternates to the user that have not been used by the user which cause the user to navigate through alternates that may not be useful to the user.
In view of the foregoing, methods and systems for learning to reorder alternates based on a user's personalized vocabulary are provided. Furthermore, there is need to apply statistical learning methodologies to reorder an alternate list based on a user's vocabulary usage and also to suppress or bias against words that have never been used. It is with respect to these and other considerations that the present invention has been made.